Various kinds of methods have been used or proposed in the literature to measure the size, shape, and internal volumes of specific organs using ultrasound segmentation analysis from three-dimensional ultrasound images. Examples of body structures undergoing segmentation analysis include heart chambers, particularly cardiac chambers (left and right ventricles, left and right atriums, the prostate, the bladder, and amniotic sacs.
The measurement of heart chambers is described, for example in U.S. Pat. No. 6,346,124 to Geiser, et al. (Autonomous Boundary Detection System For Echocardiographic Images). Similarly, the measurement of bladder structures are covered in U.S. Pat. No. 6,213,949 to Ganguly, et al. (System For Estimating Bladder Volume) and U.S. Pat. No. to 5,235,985 to McMorrow, et al., (Automatic Bladder Scanning Apparatus). The measurement of fetal head structures is described in U.S. Pat. No. 5,605,155 to Chalana, et al., (Ultrasound System For Automatically Measuring Fetal Head Size). The measurement of fetal weight is described in U.S. Pat. No. 6,375,616 to Soferman, et al. (Automatic Fetal Weight Determination).
Most of these techniques are not ideal in that they cannot be generalized beyond the application for which they were designed. They use shape or size assumptions that are not necessarily satisfied for different applications. An attempt to make a generalized model is described in U.S. Pat. No. 5,588,435 to Weng, et al. (System And Method For Automatic Measurement Of Body Structures) but is limited in its application due to method complexity and operational robustness.
The techniques commonly employed in segmentation use active contour modeling and live wire methods to delineate an object shape within electronic-based images, and partial differential equation (PDE) based processing of image signals to reduce background noise speckle. These three methods have their advantages and disadvantages. For example, the active contour modeling method or “snakes” is a region-based boundary method and requires specific shape assumptions and the use of deformable models to work properly (M. Kass, A. Witkin, D. Terzopolous, “Snakes: Active Contour Models,” International Journal of Computer Vision, pp. 321–331, 1988). However, “snake” methods require an initial contour that needs to be very close to the final desired boundary. In addition, most of these methods are iterative in nature and labor intensive.
Live wire methods, championed by A. X. Falacao et al. in “User-steered Image Segmentation Paradigms: Live wire and Live Lane,” (A. X. Falacao, J. K. Udupa, S. Samarasekara, and S. Sharma, Graphical Models and Image Processing, 60, 233–26-, 1998) and E. W. Dijkstra, in “A note on two problems in connection with graphs,” (Numerical Math, vol. 1, pp. 269–271, 1959), requires a user to actively provide a starting point and an ending point via a mouse, pointer, or equivalent delineation means. Live wire is an interactive segmentation method which uses a minimum cost path between the last clicked user point to the current mouse clicked location. The live wire methodology of Falacao is labor intensive since it requires an iterative approach and generates a series of mouse delineating end-points. Furthermore, live wire methods are limited in accuracy because the simplest optimal path contour connecting any two mouse determined end-points is not sufficient in ultrasound images that are prone to mouse pointer backtracking. That is, the “user-steering” methods of Falacao require that the user backtrack the mouse whenever the drawn contour line has lost track of the object boundary as presented on the ultrasound image.
Most ultrasound processing methods involve some pre-processing techniques to reduce speckle noise and enhance images, such as median filtering methods and PDE-based image processing. PDE-based methods have the added advantages of preserving edges while enhancing images (see P. Perona and J. Malik, “Scale-space and edge detection using aniostropic diffusion,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, July 1990, pp. 629–639; J. A. Sethian, Level Set Methods and Fast Marching Methods, 2nd Edition, Cambridge University Press, 1999; S. Osher and L. I. Rudin, “Feature-oriented image enhancement using shock filters,” SIAM Journal of Numerical Analysis, vol. 27, pp. 919–940, August 1990; and U.S. Pat. No. 5,644,513 to Rudin, et al. “System Incorporating Feature-Oriented Signal Enhancement Using Shock Filters”). These methods, especially shock filters for image sharpening or blind deconvolution, have been used reducing noise speckle for computerized-tomography (CT), magnetic resonance (MR), and positron emission-tomography (PET) images. However, shock filters implemented with unoptimized algorithms have been found deficient in that the unoptimized algorithms increase or enhance noise speckle, thereby degrading the CT, MR, and PET images.
In ultrasound images, chamber-like structures (for example: bladder, heart left ventricle, prostate, amniotic sac) are further prone to image degradation due to ultrasound echoes reflecting from other surfaces parallel to do organ wall boundary. The echoes from the parallel surfaces present as bright regions overlapping with the dark chamber boundary walls. Thus, ultrasound-echoing overlaps creates missing lateral boundaries of the organ walls. Thereby, making it difficult to accurately determine boundary wall locations where segmentation lines need to be drawn.
Thus, there is a need for a generalized image analysis method to accurately measure the size, shape, and volumes of fluid-filled and non-fluid filled organs from analysis of one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) images from ultrasound, CT, MR, and PET procedures. The generalized image analysis method requires optimal algorithms to minimize background noise speckle and maximize structure segmentation or delineation by minimizing the creation of missed lateral boundaries of organ structures. The generalized image analysis method needs to be broadly adaptable and simple to implement.